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List:       r-sig-mixed-models
Subject:    Re: [R-sig-ME] Orthogonal vs. Non-orthogonal contrasts
From:       Thierry Onkelinx <thierry.onkelinx () inbo ! be>
Date:       2016-05-30 7:47:06
Message-ID: CAJuCY5xAhBfQKrsK+ivQkCTTmPd=1kvSDYWDh_YZP4kekkUZ2g () mail ! gmail ! com
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Dear Yasu,

I see two advantages of multcomp:

1) It can work with any parametrisation. So it doesn't matter whether you
use dummy encoding or some kind of contrast. Hence you can do any post-hoc
test without having to refit the model. Note that the specification of the
contrast will depend on the parametrisation. I find dummy encoding easier
to generate post-hoc contrasts.

2) It corrects for multiple testing.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2016-05-30 9:30 GMT+02:00 Yasuaki SHINOHARA <y.shinohara@aoni.waseda.jp>:

> Dear Thierry,
>
> Thank you very much for your reply.
>
> I understood that the results of each main fixed factor (e.g., Factor A,
> B, C and D) are pointless, since the interaction factors affected the
> results of the main fixed factors. Actually, I manually coded the contrasts
> for all the fixed factors based on the hypothesis I wanted to test, as
> follows.
>
> #Factor A (testing block)
> PreVsMid<-c(1,-1,0)
> PreVsPost<-c(-1,0,1)
> contrasts(alldata$FactorA)<-cbind(PreVsMid,PreVsPost)
> #Factor B (Trainer order)
> IDVsDIS<-c(1,-1)
> contrasts(alldata$FactorB)<-cbind(IDVsDIS)
> #Factor C (Phonetic environment)
> IniVsMid<-c(1,-1,0)
> IniVsCls<-c(-1,0,1)
> contrasts(alldata$FactorC)<-cbind(IniVsMid, IniVsCls)
> #Factor D (Length of Experience, continuous variable)
> alldata$FactorD<-as.numeric(alldata$FactorD)
>
> What I really wanted to test is the interaction between Factor A and
> Factor B. Factor A has the two contrasts (i.e., PreVsMid (1,-1,0),
> PreVsPost(-1,0,1)), and Factor B has only one contrast (i.e., IDvsDIS
> (-1,1)) since there are only two levels in the Factor B. I tested whether
> there is a significant difference in the contrast of PreVsMid between the
> two levels of Factor B (IDvsDIS). Therefore, I did not use the dummy
> (simple effect) coding but used the effect coding.
>
> As you suggested, I tried to figure out how to use the multcomp package. I
> found that the glht function in the packages allows me to test a variety of
> contrasts with a matrix. However, I felt that the contrasts I coded above
> are maybe enough to test my hypothesis, and I am wondering whether I should
> use the glht function for the contrasts.
>
> Could you please let me know if there are any advantage of using the glht
> function?
>
> I really appreciate your help.
>
> Best wishes,
> Yasu
>
>
> On Thu, 26 May 2016 08:53:44 +0200
>
>  Thierry Onkelinx <thierry.onkelinx@inbo.be> wrote:
>
>> Dear Yasu,
>>
>> The contrast x = c(1, -1, 0) is equivalent to beta_x * 1 * a_1 + beta_x *
>> (-1) * a_2 + beta_x * 0 * a_3.
>> Likewise contrast y = c(.5, -.5, 0) is equivalent to beta_y * 0.5 * a_1 +
>> beta_y * (-0.5) * a_2 + beta_y * 0 * a_3.
>>
>> Since both model the same thing beta_x * 1 * a_1 + beta_x * (-1) * a_2 +
>> beta_x * 0 * a_3 = beta_y * 0.5 * a_1 + beta_y * (-0.5) * a_2 + beta_y * 0
>> * a_3.
>> Some simple math will show that beta_x = 2 * beta_y
>>
>> Your contrasts are correct but pointless given your model. They are only
>> meaningful in case FactorA is only a main effect. You included FactorA in
>> some interactions as well. So you'll need to define contrasts on the full
>> set of fixed parameters to get some sensible results. You can do that with
>> the multcomp package. I would also suggest that you find some local
>> statistician to help you define the contrasts relevant for your model.
>>
>> Best regards,
>>
>>
>> ir. Thierry Onkelinx
>> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
>> Forest
>> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
>> Kliniekstraat 25
>> 1070 Anderlecht
>> Belgium
>>
>> To call in the statistician after the experiment is done may be no more
>> than asking him to perform a post-mortem examination: he may be able to
>> say
>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>> The plural of anecdote is not data. ~ Roger Brinner
>> The combination of some data and an aching desire for an answer does not
>> ensure that a reasonable answer can be extracted from a given body of
>> data.
>> ~ John Tukey
>>
>> 2016-05-26 6:31 GMT+02:00 Yasuaki SHINOHARA <y.shinohara@aoni.waseda.jp>:
>>
>> Dear Thierry,
>>>
>>> Thank you very much for your reply.
>>> I understood why. The interaction of blockPreVsMid:FactorD turned
>>> significant in the model which contrasted the testing block factor as
>>> PreVsMid and PreVsPost (i.e.,cbind(c(1,-1,0),c(-1,0,1))), although the
>>> interaction was not significant in the model with the testing block
>>> contrasted as PreVsMid and PreMidVsPost (i.e.,
>>> cbind(c(1,-1,0),c(1,1,-2))).
>>>
>>> Could I ask another question?
>>> What is the difference in making a contrast of PreVsMid as c(1,-1,0) and
>>> as c(0.5, -0.5, 0)?
>>> It seems that the beta and SE are double if I code the contrasts with
>>> (0.5, -0.5, 0). I hope it does not matter.
>>>
>>> Also, I coded "contrasts(data$FactorA)<-cbind(c(1,-1,0),c(-1,0,1))" to
>>> test the differences between the mean of level 1 vs. the mean of level 2
>>> and between the mean of level 1 and the mean of level 3. Is this correct?
>>> Some website says something different from what I understood (e.g., the
>>> first Answer of
>>>
>>> http://stats.stackexchange.com/questions/44527/contrast-for-hypothesis-test-in-r-lmer
>>> ).
>>>
>>> My model includes both categorical and numeric variable, and all
>>> categorical variables were coded manually.
>>>
>>> Best wishes,
>>> Yasu
>>>
>>>
>>> On Wed, 25 May 2016 09:44:14 +0200
>>>  Thierry Onkelinx <thierry.onkelinx@inbo.be> wrote:
>>>
>>> Dear Yasu,
>>>>
>>>> A is part of two interactions. Hence you cannot interpret this main
>>>> effect
>>>> without the interactions. Note that changing the contrast will also
>>>> effect
>>>> the interactions.
>>>>
>>>> Best regards,
>>>>
>>>> ir. Thierry Onkelinx
>>>> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
>>>> and
>>>> Forest
>>>> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
>>>> Kliniekstraat 25
>>>> 1070 Anderlecht
>>>> Belgium
>>>>
>>>> To call in the statistician after the experiment is done may be no more
>>>> than asking him to perform a post-mortem examination: he may be able to
>>>> say
>>>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>>>> The plural of anecdote is not data. ~ Roger Brinner
>>>> The combination of some data and an aching desire for an answer does not
>>>> ensure that a reasonable answer can be extracted from a given body of
>>>> data.
>>>> ~ John Tukey
>>>>
>>>> 2016-05-25 4:42 GMT+02:00 Yasuaki SHINOHARA <y.shinohara@aoni.waseda.jp
>>>> >:
>>>>
>>>> Dear all,
>>>>
>>>>>
>>>>> Hello, I am doing research of second language acquisition.
>>>>> I am wondering about the glmer in R for my analyses. Could you please
>>>>> answer my question?
>>>>>
>>>>> I have the following logistic mixed effects model.
>>>>> model<-glmer(corr ~ A + B + C + D + A:B + B:C + A:D +(1+A|subject) +
>>>>>
>>>>>
>>>>> (1+A|item:speaker),family=binomial,data=mydata,control=glmerControl(optimizer="bobyqa",
>>>>> optCtrl=list(maxfun=1000)))
>>>>>
>>>>> I tested language learners (subjects) three time (pre-training,
>>>>> mid-training, post-training) with the "item" produced by "speaker", so
>>>>> Factor A is "testing block" which has three levels (i.e., pre, mid,
>>>>> post).
>>>>> Since each subject took the test three times, the random slopes for the
>>>>> Factor A were also included as a random factor.
>>>>>
>>>>> I made an orthogonal contrast for the Factor A (testing block) as
>>>>> follows.
>>>>> PreVsMid<-c(1,-1,0)
>>>>> PreMidVsPost<-c(1,1,-2)
>>>>> contrasts(mydata$A)<-cbind(PreVsMid,PreMidVsPost)
>>>>>
>>>>> The results from summary(model) function for this factor were as
>>>>> follows.
>>>>> pre vs. mid test: β = 0.22, SE = 0.05, z = 4.34, p < 0.001
>>>>> pre & mid vs. post test: β = -0.21, SE = 0.04, z = -5.96, p < 0.001.
>>>>>
>>>>> However, I thought it would be better if I made a non-orthogonal
>>>>> contrast
>>>>> for this factor as "pre vs. mid" and "pre vs. post" to test my
>>>>> hypothesis.
>>>>> So I made a new contrast for the Factor A as follows.
>>>>> PreVsMid<-c(1,-1,0)
>>>>> PreVsPost<-c(1,0,-1)
>>>>> contrasts(mydata$A)<-cbind(PreVsMid,PreVsPost)
>>>>>
>>>>> The results from summary(model) function for this contrast were
>>>>> pre vs. mid test: β = -0.01, SE = 0.04, z = -0.14, p > 0.05 (=0.89),
>>>>> pre vs. post test: β = 0.42, SE = 0.07, z = 5.96, p < 0.001.
>>>>>
>>>>> Although the first contrast (pre vs. mid) is the same for both models,
>>>>> why
>>>>> the results of pre vs. mid contrast are so different (one is very
>>>>> significant, but the other one is not significant)?
>>>>>
>>>>> I really appreciate any help.
>>>>>
>>>>> Best wishes,
>>>>> Yasu
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-mixed-models@r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>
>>>>>
>>>>>

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